Learn to scale your UNIX® Python applications to multiple cores by using the multiprocessing module which is built into Python 2.6. Multiprocessing mimics parts of the threading API in Python to give the developer a high level of control over flocks of processes, but also incorporates many additional features unique to processes.

In a previous article for IBM® developerWorks®, I demonstrated a simple and effective pattern for implementing threaded programming in Python. One downside of this approach, though, is that it won't always speed up your application, because the GIL (global interpreter lock) effectively limits threads to one core. If you need to use all of the cores on your machine, then typically you will need to fork processes, to increase speed. Dealing with a flock of processes can be a challenge, because if communication between processes is needed, it can often get complicated to coordinate all of the calls.
Fortunately, as of version 2.6, Python includes a module called "multiprocessing" to help you deal with processes. The API of the processing module has some similarities to the way the threading API works, but there are also few differences to keep in mind. One of the main differences is that processes have subtle underlying behavior that a high-level API will never be able to completely abst